This opening slide introduces one of the most critical challenges in production AI systems performance.
No matter how intelligent an LLM system is, users judge it primarily by how fast and reliable it feels.
Latency and scalability are therefore not secondary concerns.
They define whether users trust and adopt the system.
The visual on this slide highlights interconnected infrastructure, reinforcing the idea that LLM applications
are distributed systems by nature.
Requests flow through APIs, retrieval pipelines, model inference, and post-processing layers, each
contributing to overall response time.
This section focuses on engineering systems that feel responsive under real world conditions.
We are not chasing microsecond optimizations, but designing architectures that remain stable as traffic
grows and workloads become more complex.
By the end of this section, you will understand how to reason about latency holistically, how to choose
the right inference strategies, and how to scale LLM applications without sacrificing reliability or
user experience.
This slide explains why latency is fundamentally a user experience problem, not just a technical metric.
Modern users expect near instant feedback when response times become inconsistent or exceed a few seconds.
Users perceive the system as unreliable, even if it eventually returns a correct answer.
The slide emphasizes an important psychological insight perceived performance matters more than raw
milliseconds.
A system that responds consistently feels trustworthy, while one that is occasionally slow erodes confidence.
Variability is more damaging than absolute delay.
Scaling compounds these challenges as traffic increases.
Infrastructure stress, tool integrations and concurrency introduce additional latency layers.
Systems that succeed are designed for scale from day one rather than retrofitted after problems appear.
The key takeaway is clear trust is built through predictability.
Engineering for low and consistent latency is not optional.
It is essential for adoption, retention, and long term success.
This slide breaks down where latency actually comes from in LM applications.
Importantly, latency is not caused by a single bottleneck it compounds across the entire request lifecycle.
The first contributor is network calls to LM providers, often adding hundreds of milliseconds before
inference even begins.
Next comes context assembly, where retrieval, augmented generation, memory lookups, and prompt construction
can add seconds.
Model inference time varies depending on model size, context length, and provider load.
more advanced systems introduce tool execution latency, where external APIs or databases are called
during agent loops.
After generation, post-processing adds additional overhead for parsing, formatting, and validation.
Finally, cold starts from serverless or containerized environments introduce unpredictable delays.
The critical insight highlighted at the bottom of the slide is that these latencies stack in complex
agent workflows.
What starts as seconds can easily become minutes, if not carefully managed.
This slide introduces a key architectural decision whether to use streaming or batch inference.
While both approaches generate the same content, they fundamentally change how users experience the
system with streaming inference.
Tokens are sent incrementally as they are generated.
This dramatically reduces perceived latency, often by 60 to 80% because users see progress immediately.
Streaming is ideal for chat interfaces and interactive tools where responsiveness matters.
However, it requires infrastructure support such as WebSockets or server sent events.
Batch inference, on the other hand, returns the full response only after generation completes.
This maximizes throughput and simplifies error handling, making it ideal for offline jobs.
Analytics and background processing.
Batch responses are also easier to cache and post-process.
The golden rule on this slide is critical optimize for user experience first, not raw speed.
A slower system that streams feels faster than a slightly quicker system that stays silent until completion.
This slide provides a practical framework for deciding between streaming and batch inference.
The first question is user context.
Is a human actively waiting.
Or is this a background task?
If a person is watching, the screen, streaming almost always improves experience.
Next is latency sensitivity.
Interactive applications benefit from immediate feedback, while backend pipelines often tolerate delays
measured in minutes.
The third factor is system design.
Streaming requires connection management and stateful infrastructure, which may not be feasible in
all environments.
The slide clearly outlines when to choose each approach.
Streaming is best for chat assistance and tools, where users may interrupt or provide feedback.
Batch processing is ideal for document ingestion, ETL pipelines, and analytics.
The best practice at the bottom reinforces architectural flexibility.
Support both modes.
User facing endpoint stream while background workers batch.
This separation allows you to optimize each workload Independently.
This slide explains why asynchronous processing is not an optimization, but a requirement for scalable
LM systems.
LM calls are slow, often taking seconds, and they are I o bound, meaning the server spends most of
its time waiting.
Blocking on these calls wastes resources and limits concurrency with blocking architectures.
Systems often fail at surprisingly low user counts because each request monopolizes a thread.
Async processing solves this by freeing request threads while waiting for responses.
Modern async runtimes can handle context switching with minimal overhead, enabling orders of magnitude
more concurrent requests on the same hardware.
The metrics on this slide highlight the impact up to 100 times, more concurrency improvements, and
dramatically better resource utilization.
The quote at the bottom captures the mindset shift.
Async is not a performance tweak, it is the foundation of any LLM application expecting real traffic.
This slide explores practical async patterns used in real world LLM systems.
Async HTTP calls allow concurrent requests to LLM providers and are essential for all real time applications.
Background.
Workers decouple request handling from processing, enabling immediate user feedback while work continues
asynchronously.
Tasks like celery Sidekiq or bull MQ distribute work across worker pools and provide retry logic and
monitoring.
These patterns are especially valuable for long running or failure prone tasks.
The real world use cases reinforce these ideas.
Long running agents with tool calls should return task IDs rather than blocking users.
Document ingestion and embedding generation belong entirely in background jobs.
Batch embeddings and scheduled analytics benefit from parallelized async execution.
The key message is that different workloads require different async patterns.
Choosing the right one is an architectural decision that directly affects scalability and reliability.
This slide focuses on reliability under pressure.
Even perfectly async systems can fail without proper load management.
Traffic spikes, abuse, or downstream failures can trigger cascading outages if left unchecked.
The design philosophy presented here is important.
The goal is not infinite scalability, but graceful degradation.
Serving most users well is better than failing for everyone.
The slide outlines defensive techniques, rate limiting controls, request velocity, and prevents abuse.
Request queuing buffers spikes instead of rejecting traffic outright.
Back pressure signals upstream systems to slow down before queues.
Overflow timeouts.
Free resources from stuck requests and circuit breakers.
Prevent repeated calls to failing services.
Together, these mechanisms protect both infrastructure and user experience.
Load management is about resilience, not just performance.
The final slide emphasizes that true scaling is architectural, not just infrastructural.
Simply adding servers without redesigning system structure only scales problems.
The slide breaks scaling into layers.
The API layer should be stateless and horizontally scalable behind load balancers.
Worker pools should be separated by workload type and auto scaled based on queue depth.
Caching layers reduce repeated computation and improve response times.
Model routing strategies match request complexity to appropriate models.
The critical insight at the bottom is the most important takeaway of this section.
Scaling requires thoughtful design across every layer.
When architecture is sound, adding infrastructure amplifies success rather than chaos.